Multi-agent LLM simulations testing algorithmic collusion and coordination breakdown in oligopoly markets.
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Updated
Jul 9, 2025 - Jupyter Notebook
Multi-agent LLM simulations testing algorithmic collusion and coordination breakdown in oligopoly markets.
Python replication of Calvano et al. (2020)
Q-learning oligopoly simulation (Calvano et al., 2020) — high-fidelity Python reproduction with ≥ 95 % accuracy
Replication of Calvano et al. (2020) 'Artificial Intelligence, Algorithmic Pricing, and Collusion' (AER)
Bias Coverage Lab for RetailTech personalized-pricing / recommendation / loyalty disparate-impact testing. 6 cohort dimensions, 4-band verdict, aligned with FTC Section 5 + NY ATBP + CA CPPA ADMT + EU AI Act Art 10/15.
AI Incident Card profile for RetailTech: pricing discrimination complaints, recommendation bias surfaces, biometric misidentification, refund discrim, dark patterns enforcement events. 12 event types + 8 reason codes + 4-band severity verdict.
Analysis code behind The AI Money Test. Every number in every video is produced by a script here. Source data is not redistributed - each report links to the original.
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Evidence Bundle profile for RetailTech FTC algorithmic-pricing readiness. 12 sections aligned with FTC Section 5 + 2023 algo-pricing guidance + Operation AI Comply + EU Omnibus 2019/2161 + state retail AI laws.
A portfolio of quantitative finance models developed during the JPMorgan Chase Job Simulation, featuring continuous-time commodity forecasting, algorithmic storage contract pricing, and dynamic programming for FICO score quantization.
Research code supporting my doctoral dissertation on reinforcement learning, dynamic optimization, autonomous vehicles, and algorithmic pricing.
A real-time pricing engine that transforms a bar menu into a simulated stock market.
The Fair Pricing Playbook
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